Existing gas turbine engines typically utilize a digital/electronic engine control system, often referred to as FADEC (Full Authority Digital Electronic Control). FADEC includes mathematical and computational models of various engine systems, sub-systems, and components. These mathematical/computational models are often used to predict and control the behavior of engine systems, sub-systems, and components. Prediction and control of engine behavior may utilize (1) feedback of actual engine behavior by means of sensors located in various parts of the engine (temperature, pressure, speed, etc.), (2) calculations and predictions of engine system, sub-system, and component behavior and (3) schedules describing desired or target system, sub-system, and component behavior under certain engine operating conditions.
Currently, embedded CLM tracking methods represent the major rotating components as individual modules. A tracking filter adjusts the component quality parameters in the CLM model to match the engine sensor values to the model-computed sensor values. The existing CLM methods assume that engine sensors are providing accurate information. FADEC performs sensor range, limit and signal validation. Out-of-range sensor failures are readily detected by FADEC logic but in-range sensor values are difficult to diagnose.
In order to predict and control engine behavior, the mathematical/computational models include information about the physical properties of the relevant engine systems, sub-systems, and components, such as physical size (dimensions, shape), coefficient of thermal expansion, modulus of elasticity, stiffness, time constants, and other physical, mechanical, and thermal properties. This information about physical properties is typically pre-programmed into the engine control system, and represents the physical condition of the engine system, sub-system, or component when new. During engine operation by the customer/user, changes in the physical properties of the engine systems, sub-systems, and components can occur over time. Examples of such changes are wear and distortion, which change the physical size and shape of the engine system, sub-system, or component. Such changes in physical properties often reduce or impair engine performance and efficiency, leading to increased fuel consumption, and reduced engine life. Unfavorable changes of this nature are referred to as deterioration. As an engine deteriorates and undergoes physical changes over time, the physical properties of the deteriorated engine system, sub-system, or components start to deviate from the physical properties that were originally pre-programmed into the engine control system. If direct feedback of the changing physical properties from the engine to the control system is not available (as is the case in contemporary engine control systems), then the control system cannot account for the physical changes. The resulting deviations between the deteriorated physical properties (in the engine), and the new physical properties (in the control system) introduce discrepancies into the mathematical computational models. These discrepancies impair the ability of the engine control system to accurately predict and control the behavior of the particular engine system, sub-system, or component. This can result in reduced efficiency and engine life, increased fuel consumption, and other unfavorable effects on engine performance.
The deviations between deteriorated and new physical properties are most frequently addressed by physical overhaul and maintenance, in which the physical properties are restored from the deteriorated condition to the new condition. This physical maintenance, sometimes referred to as performance restoration, is achieved either by replacement of the particular engine system, sub-system, or component with new hardware, or by physical processing (repair) of the hardware. However, physical overhaul and maintenance of this type is difficult, time consuming, inconvenient, and expensive. An effective method of addressing the control system deviation between the deteriorated and new conditions necessarily places a high degree of reliance on the engine sensors. If a sensor failure is undetected because its associated parameter is within a normal operating range, the system will track an erroneous parameter, resulting in a flawed updated model.
One method of detecting in-range sensor failure is disclosed in U.S. Pat. No. 6,314,350 B1. Sensor status monitoring logic compares current status of a sensor to previous status and generates a transition count indicating the number of times during a flight that each monitored sensor changed status. A time duration table records the amount of time status is recorded in each of its possible states. When the transition counter exceeds a predetermined threshold, the maintenance logic uses the transition counter output to generate a real-time maintenance message. The time duration table is also used to detect a pattern from the table so a type of default can be automatically detected and an appropriate post-flight maintenance message can be generated. The method detects intermittence, which may forecast sensor failures including in-range sensor failures, but the method assumes a fault based upon threshold settings, which may not accurately forecast a failure, resulting in unnecessary maintenance messages.
Therefore, there is a need for a diagnostic system for detecting in-range sensor faults by observing the tracked component qualities in an embedded model and recognizing anomalous patterns of quality changes corresponding to sensor errors.